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Module Title:
Module Code:
Academic Year / Semester:
ASSESSMENT BRIEF
Al Fundamentals
KV4004
2023-24/Semester 1
% Weighting (to overall module):
50%
Assessment Title:
Date of Handout to Students:
Mechanism for Handout:
Microsoft Azure practical solution to selected Al
topic
9th October 2023
Module Blackboard Site & Online session
Mechanism for Submission:
Submission Format / Word Count
A report that presents the practical work done in
the first assessment, the implementation approach
and a discussion of the findings.
The word limit for the report is 1000 words, not
including the front cover, table of contents page,
references and appendices.
Mechanism for return of Feedback
and Marks:
Mark and individual written feedback will be
uploaded to the Module Site on Blackboard. For
further queries please email module tutor.
LEARNING OUTCOMES
The learning outcomes (LOS) for this module are: -
Knowledge & Understanding
MLO1 Demonstrate knowledge, and basic understanding, of essential facts, concepts,
principles, theories, techniques, and technologies related to computing, computer science, data
science and Artificial Intelligence (AI) workloads.
MLO2 Describe Al workloads and considerations, fundamental principles, features of AI
workloads and their implementation on Azure.
MLO3 Specify, design and construct simple computer, AI and data-based systems.
Intellectual / Professional skills & abilities MLO4 Critically Specify, design and construct simple computer- and AI- based systems
(IPSA).
Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA)
MLO5 Demonstrate a basic awareness of the global, ethical, and cultural issues related to
computing, and specifically AI and data - and its societal implications for equality and diversity.
This assessment addresses learning outcomes MLO3, MLO4 and ML05.
Instructions to students:
This is an individual piece of work, and you must not work with others to construct your work. During
the semester there are numerous opportunities to seek and get advice and support on your work,
from tutors and peers but you must ensure you do not do work for others or copy work from others.
Submission Requirements
You must comply to the following criteria to fulfil the assignment submission requirements:
The word limit for the report is 1500. However, if the assignment is within +10%
(i.e., up to 150 words) then NO penalty will be applied.
The word count should be declared on the cover page of your assignment. The
word count does not include title page, table of contents page, references and
appendices and in text citations [e.g. (O'Brien, 2020)].
Academic Conduct:
You must adhere to the university regulations on academic conduct. Formal inquiry proceedings will
be investigated if there is any suspicion of misconduct or plagiarism in your work. Refer to the
University's regulations on assessment if you are unclear as to the meaning of these terms. The latest
copy is available on the university website.
If
you need an extension:
Contact ask4Help. Tutors and Module tutors cannot grant extensions.
Make sure that your report is submitted on time. University regulations state that assignments
submitted late without approval will incur a 10% reduction for the first 24hours then a zero mark after
this. You may apply for an extension of time to complete assessed coursework if there are personal
circumstances which are unforeseen and unpreventable and have a serious effect on your ability to
submit the work by the published hand-in deadline. You must submit an 'Application for Authorisation
for Late Submission of Assessed Work' before the hand-in deadline. Appropriate medical certification,
or other relevant evidence confirming the circumstances, must be provided. Information regarding
this policy and procedure can be accessed through your student portal.
Disabled students
Contact the module lead tutor about reasonable adjustments.
Errors
If any errors are found in this document, changes will be posted to the eLP (Blackboard). Versions will
be clearly stated. All versions will be accepted. INSTRUCTION OF ASSESSMENTS
Assessment Brief
Microsoft Azure is a cloud computing platform. It provides a wide range of
services, including the virtual machines, databases, analytics, AI and more. It offers
a robust set of services and tools for implementing AI and machine learning
solutions across various domains. It can be used to build, deploy and manage
applications and services through Microsoft's global network of data centres.
There are various applications of AI in Azure which use Azure services and tools.
These Azure services and tools extend to a wide range of applications, from image
recognition to natural language understanding to predictive analytics and
recommendation systems.
You are required to use azure machine learning that realises the practical
solution to facial recognition discussed in the first assignment. You are required
to provide the details of the implementation approach and a discussion of the
findings. You are required to conduct data preparation/transformation to make
the data ready for the model. The components you must complete are:
1. Exploration of the facial recognition dataset you have chosen in the
previous assignment such as Data preparation/transformation and some
visualisations for the data pre-processed [25 Marks]
2. The implementation of the AI including the design, choose the models,
training and validation [30 Marks]
3. The implementation of the inference model and testing the inference
model by some unseen examples [20 Marks]
4. Discussion of the findings from the AI model built i.e., the performance
metrics and their visualisations [25 Marks] Grade
70-100
%
APPENDIX A
Marking criteria
Criteria
A mark of 70% or over is indicative of excellent work where the
student has more than met the requirements of the assessment brief
and demonstrated an exceptional understanding of Al systems on
Azure and techniques along with knowledge of their chosen dataset
and provides a comprehensive critical view of the workflow of these
Al models and excellent presentation of the results by distinctive
visualisations.
60-69
60
%
40-59
%
3
30-39
%
A mark within this range is highly competent and completed to a
high standard. The work demonstrates a good level of
understanding of Al services on Azure along with knowledge of their
chosen dataset and provides a comprehensive critical view of the
workflow of these models and good presentation of the results by
visualisations. The requirements of the assessment brief have been
met to a high standard but with room for a few minor areas of
improvement. Marks at the lower end in this band suggest that
students have met all or most of the requirements of the assessment
brief but there are a larger number of minor areas needing
improvement.
A mark within the range indicates a pass, where the work has been
completed to a satisfactory standard, but where there is still
significant scope for improvement. The work demonstrates an
acceptable understanding of Al services on Azure and techniques
along with a reasonable knowledge of their chosen dataset and
provides a reasonably well-documented account of the workflow of
these models. The work will have covered most of the key
assessment criteria, but these might be at a more superficial level
compared with work in the higher mark ranges, with evidence of a
less complete understanding of the subject area. The work may
indicate that less independent learning has been performed or that
less robust methods are used.
This indicates a fail mark, where learning outcomes may not all have
been met to a satisfactory standard and where there may be a range
of omissions, poor communication and/or possibly a lack of
knowledge derived from wider reading. The work does not
demonstrate an acceptable understanding of AI services on Azure,
nor provides a well-documented account of workflow of these
models. Work in this mark range indicates insufficient evidence of